Decision Trees on the Foreign Exchange Market
In this article we present a novel approach to generate a data set directly from real-world forex market data. The data are transformed into a decision table. Every single object in such a table consists of conditional attributes—in this case values of technical analysis indicators as well as of the decision class (BUY, SELL or WAIT). Our second goal was to test the quality of the classification based on two well-known algorithms used for decision tree construction: the CART algorithm and the C4.5 algorithm. All experiments were conducted on three different currency pairs—with 3 data sets for each pair.
KeywordsForex market Decision tree CART C4.5 algorithm
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